Examining the Volatility of Taiwan Stock Index Returns via a Three-Volatility-Regime...
Li, Ming-Yuan; Lin, Hsiou-Wei
2004-10-04 00:00:00
This study adopts Hamilton and Susmel's (1994) Markov-switching ARCH (hereafter SWARCH) model to examine the volatility of the valued-weighted Taiwan Stock Index (hereafter TAIEX) returns. We also conduct sensitivity tests on comparison observations of Dow Jones and Nikkei stock indices. Our empirical findings are consistent with the following notions. First, the SWARCH model appears to outperform the competing ARCH and GARCH models in estimating the volatilities of TAIEX. Second, the three-volatility-regime setting is descriptive for TAIEX and Nikkei. In contrast with Hamilton and Susmel (1994), the contemporaneous Dow Jones adopted in this paper has only two regimes. Our test results suggest that the optimal number of volatility regimes is sensitive to the choice of sample periods. Third, our empirical results also lend an explanation to such phenomenon: the probability that TAIEX directly moves from a low (high) volatility regime to the high (low) volatility regime approaches zero, whereas TAEIX happened to be in a low volatility regime during the pre-financial-crisis period from April, 1996 to July, 1997. These can explain why Taiwan was one of Asia's few star performers compared with recession-hit neighbors during the first eighteen months of Asia's financial crisis.
http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.pngReview of Quantitative Finance and AccountingSpringer Journalshttp://www.deepdyve.com/lp/springer-journals/examining-the-volatility-of-taiwan-stock-index-returns-via-a-three-k3ZG0wC8wI

Abstract

This study adopts Hamilton and Susmel's (1994) Markov-switching ARCH (hereafter SWARCH) model to examine the volatility of the valued-weighted Taiwan Stock Index (hereafter TAIEX) returns. We also conduct sensitivity tests on comparison observations of Dow Jones and Nikkei stock indices. Our empirical findings are consistent with the following notions. First, the SWARCH model appears to outperform the competing ARCH and GARCH models in estimating the volatilities of TAIEX. Second, the three-volatility-regime setting is descriptive for TAIEX and Nikkei. In contrast with Hamilton and Susmel (1994), the contemporaneous Dow Jones adopted in this paper has only two regimes. Our test results suggest that the optimal number of volatility regimes is sensitive to the choice of sample periods. Third, our empirical results also lend an explanation to such phenomenon: the probability that TAIEX directly moves from a low (high) volatility regime to the high (low) volatility regime approaches zero, whereas TAEIX happened to be in a low volatility regime during the pre-financial-crisis period from April, 1996 to July, 1997. These can explain why Taiwan was one of Asia's few star performers compared with recession-hit neighbors during the first eighteen months of Asia's financial crisis.

Journal

Review of Quantitative Finance and Accounting
– Springer Journals

Published: Oct 4, 2004

Recommended Articles

Loading...

References

System Identification: Advance and Case Studies

Akaike, H.

Unemployment Persistence: Does the Size of the Shock Matter?

Bianchi, M.; Zoega, G.

Generalized Autoregressive Conditional Heteroskedasticity

Bollerslev, T.

Modeling the Persistence of Conditional Variance

Bollerslev, T.; Engle, R. F.

ARCH Modeling Finance, A Review of the Theory and Empirical Evidence

Bollerslev, T.; Chou, R. Y.; Kroner, K. F.

A Markov Model of Unconditional Variance in ARCH

Cai, J.

Volatility Persistence and Stock Valuation: Some Empirical Evidence Using GARCH

Chou, R. Y.

Regime Switching and Cointegration Tests of the Efficiency of Futures Markets